BigHat Biosciences

Machine Learning Engineer

• Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
• Identify opportunities for improvement in our ML tooling, and help to set strategy for ML research, driven by a strong high-level understanding of real-world drug development challenges
• Develop, refine, and deploy *de novo* design methods for generating initial hits to challenging, therapeutically interesting targets.
• Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
• Maintain an in-depth understanding of the current state-of-the-art in ML-driven protein engineering, both in the literature and at BigHat.
• Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.
• Provide technical guidance and mentorship to other ML and data science FTEs and interns.
• Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
• Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models to ongoing drug development programs.
• Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize ML methods development accordingly.

Principal Machine Learning Scientist